Name
Abstract | ||
|---|---|---|
In this paper we propose a method for reverse engineering the features of Ajax-enabled web applications. The method first collects instances of the DOM trees underlying the application web pages, using a state-of-the-art crawling framework. Then, it clusters these instances into groups, corresponding to distinct features of the application. The contribution of this paper lies in the novel DOM-tree similarity metric of the clustering step, which makes a distinction between simple and composite structural changes. We have evaluated our method on three real web applications. In all three cases, the proposed distance metric leads to a number of clusters that is closer to the actual number of features and classifies web page instances into these feature-specific clusters more accurately than other traditional distance metrics. We therefore conclude that it is a reliable distance metric for reverse engineering the features of Ajax-enabled web applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CSMR.2013.25 | CSMR |
Keywords | Field | DocType |
ajax-enabled web applications,pattern clustering,application web page,real web application,ajax-enabled web application,reliable distance,feature detection,metric lead,dom-tree similarity metric,reverse engineering,feature-specific cluster,hierarchical agglomerative clustering,silhouette coefficient,proposed distance,web sites,traditional distance metrics,internet,dom tree,clustering step,web page similarity metrics,l method,crawling framework,web page instance,actual number,feature extraction,clustering algorithms,html,web pages,measurement | Data mining,Crawling,Web page,Correlation clustering,Computer science,Reverse engineering,Metric (mathematics),Ajax,Web application,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1534-5351 | 978-1-4673-5833-0 | 1 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natalia Negara | 1 | 7 | 1.54 |
| Nikolaos Tsantalis | 2 | 743 | 32.14 |
| Eleni Stroulia | 3 | 2195 | 179.09 |